Abstract
This work presents a procedure to automate the design of Si-integrated radio frequency (RF) discrete tuning varactors (RFDTVs). The synthesis method, which is based on evolutionary algorithms, searches for optimum performance RF switched capacitor array circuits that fulfill the design restrictions. The design algorithm uses the c-dominance concept and the maximin sorting scheme to provide a set of different Solutions (circuits) well distributed along an optimal front in the parameter space (circuit size and component values). Since all the solutions present the same performance, the designer call Select the circuit that is best suited to be implemented in a particular integration technology. To assess the performance of the synthesis procedure, several RFDTV circuits, provided by the algorithm, were designed and simulated rising a 0.18 mu m CMOS technology and the Cadence Virtuoso Design Platform. The comparisons between the algorithm and circuit; simulation results show that they are very close, pointing out that; the proposed design procedure is a powerful design tool.

Abstract
This article reports the study of fractional dynamics during the evolution of a Particle Swarm Optimization (PSO) algorithm. Some initial swarm particles are randomly changed, for stimulating the system response, and its effect is compared with a non-perturbed reference. The perturbation effect in the PSO evolution is observed in the perspective of the fitness time behavior of the best particle. The dynamics is represented through the median of a sample of experiments, while adopting the Fourier analysis for describing the phenomena. The influence of the PSO parameters influence upon the global dynamics is also analyzed.

Abstract
Obtaining a well distributed non-dominated Pareto front is one of the key issues in multi-objective optimization algorithms. This paper proposes a new variant for the elitist selection operator to the NSGA-II algorithm, which promotes well distributed non-dominated fronts. The basic idea is to replace the crowding distance method by a maximin technique. The proposed technique is deployed in well known test functions and compared with the crowding distance method used in the NSGA-II algorithm. This comparison is performed in terms of achieved front solutions distribution by using distance performance indices.

Abstract
This work addresses the signal propagation and the fractional-order dynamics during the evolution of a genetic algorithm (GA). In order to investigate the phenomena involved in the GA population evolution, the mutation is exposed to excitation perturbations during some generations and the corresponding fitness variations are evaluated. Three distinct fitness functions are used to study their influence in the GA dynamics. The input and output signals are studied revealing a fractional-order dynamic evolution, characteristic of a long-term system memory.

Abstract
This work addresses the signal propagation and the fractional-order dynamics during, the evolution of a genetic algorithm (GA), for generating a robot manipulator trajectory. The GA objective is to minimize the trajectory space/time ripple without exceeding the torque requirements. In order to investigate the phenomena involved in the GA population evolution, the mutation is exposed to excitation perturbations and the corresponding fitness variations are evaluated. The chaos-like noise and the input/output signals are studied revealing a fractional-order dynamics, characteristic of a long-term system memory.

Abstract
Generating manipulator trajectories considering multiple objectives and obstacle avoidance is a non-trivial optimization problem. In this paper a multi-objective genetic algorithm based technique is proposed to address this problem. Multiple criteria are optimized considering up to five simultaneous objectives. Simulation results are presented for robots with two and three degrees of freedom, considering two and five objectives optimization. A subsequent analysis of the spread and solutions distribution along the converged non-dominated Pareto front is carried out, in terms of the achieved diversity.